Terminal X tackles memory debt by turning senior investors’ judgment into reusable AI agents. Its Capital Memory OS lets forward‑deployed engineers—former JPMorgan and Blackstone pros—work onsite to encode each firm’s investment logic into hundreds of agents. OpenAI’s GPT‑6.1 Sol handles judgment‑heavy stages such as synthesizing theses and assessing mandate fit, while GPT‑6 Luna runs high‑volume steps where token efficiency matters.
An Eval Agent grades every output against a 54‑criterion rubric, catching about 25% more out‑of‑mandate recommendations and cutting query costs by roughly 30% versus the next‑best model. Customers store decisions in a private data room, creating a versioned Memory that includes past analyses, committee decisions, and workflow instructions. One private‑equity client reported $2.4 million in annualized savings from automated due diligence and memo workflows.
Co‑founder and CEO Hyun Hong notes that GPT‑6.1 Sol’s long‑context reasoning makes evaluation pipelines deeper, while CTO Ken Lee says every corrected answer teaches the system more about the firm’s processes. The platform turns each Memory into an auditable, repeatable workflow, ensuring production queries are traceable from sources to reasoning.
Source: OpenAI Blog · Summarized by HeadlinesBriefing